Paper tackles speaker verification by removing reverberation using deep LSTM networks.
problem Improving speaker verification accuracy in reverberant environments.
method Dual-label deep LSTM networks trained to map reverberant to clean speech features.
result Evaluates performance using EERs, showing improved accuracy.
Proposes a DOA estimation method using IVs and DNNs for noise and reverberation reduction.
problem Accuracy of IV-based DOA estimation degrades due to noise and reverberation.
method Combines IV-based DOA estimation with DNNs for denoising and dereverberation.
result Average DOA error of 0.528 degrees, outperforming conventional methods.
Study shows reverberant phase is not essential for weakly-supervised dereverberation.
problem Evaluating the role of reverberant phase in weakly-supervised dereverberation.
method Statistical Wave Field Theory and recent weak supervision framework.
result Wet phase carries limited useful information and is not essential for weakly supervised dereverberation.
Jointly reduces echo, reverberation, and noise using neural networks.
problem Simultaneous reduction of acoustic echo, reverberation, and noise.
method Multichannel Gaussian modeling and neural network for joint optimization of filters.
result Outperforms individual and joint non-spectral models.
Enhances speech from reverberated and noisy signals using Wide Residual Networks.
problem Improving intelligibility of speech from reverberated and noisy signals.
method Wide Residual Network architecture with residual connections for processing contextually correlated representations.
result Enhanced speech from the proposed method succeeded for both enhancement and speech recognition tasks.
This paper proposes RAS, a novel unsupervised loss function for speech separation.
problem Unsupervised speech separation without teacher models or synthesized mixtures.
method Train a neural network to separate sources given one channel, using Wiener filtering and SI-SDR.
result Achieves significant SDR improvement with minimal labeled data.
This article addresses the modeling of reverberant recording environments in the context of under-determined convolutive blind source separation. We model the contribution of each source to all mixture channels in the time-frequency domain as a zero-mean Gaussian random variable whose covariance encodes the spatial cha…
Study neural networks learning from noisy examples via reverberation.
problem Learning from noisy examples in neural networks.
method Adapted Guerra's interpolation technique to provide statistical mechanics of supervised and unsupervised learning.
result Full phase diagrams and thresholds for learning are analytically obtained.
Method generates audio attacks resistant to reverberation and noise.
problem Physical attacks on speech recognition models.
method Simulates playback and recording transformations to generate robust adversarial examples.
result Adversarial examples can attack speech recognition without being noticed by humans.
Unified deep learning framework improves SV in noisy, reverberant, and long non-speech segments.
problem Robust speaker verification in adverse environments, especially short speech segments.
method Feature Pyramid Module (FPM)-based Multi-scale Aggregation (MSA), Self-adaptive Soft VAD (SAS-VAD), Masking-based Speech Enhancement (SE).
result The proposed method outperforms baseline systems in challenging conditions.
We propose a spatial diffuseness feature for deep neural network (DNN)-based automatic speech recognition to improve recognition accuracy in reverberant and noisy environments. The feature is computed in real-time from multiple microphone signals without requiring knowledge or estimation of the direction of arrival, an…
Quaternion neural networks improve distant speech recognition.
problem Challenges in distant speech recognition due to noise and reverberation.
method Quaternion neural networks process multi-channel audio signals as quaternion entities, capturing internal and external dependencies.
result QLSTM outperforms real-valued LSTM on multi-channel distant speech recognition tasks.
Improved speech separation and enhancement using neural beamforming.
problem Challenging speech separation and enhancement in reverberant environments.
method Sequential neural beamforming combining spectral and spatial separation methods.
result Average improvement of 2.75 dB in scale-invariant signal-to-noise ratio and 14.2% absolute reduction in speech recognition metric.
In the last years, increasing efforts have been put into the development of effective stress tests to quantify the resilience of financial institutions. Here we propose a stress test methodology for central counterparties based on a network characterization of clearing members, whose links correspond to direct credits …
In this paper we will try to assess the multifractality displayed by the high-frequency returns of Madrid's Stock Exchange IBEX35 index. A Multifractal Detrended Fluctuation Analysis shows that this index has a wide singularity spectrum which is most likely caused by its long memory. Our findings also show that this lo…
The study examines how social biases are reinforced in machine learning models used for credit scoring.
problem Reinforcement of societal biases in machine learning algorithms for credit scoring.
method Analysis of machine learning models predicting gender or ethnicity based on loan applications data.
result Machine learning models can reflect and reinforce social biases present in the data.
Financial contagion from liquidity shocks has being recently ascribed as a prominent driver of systemic risk in interbank lending markets. Building on standard compartment models used in epidemics, in this work we develop an EDB (Exposed-Distressed-Bankrupted) model for the dynamics of liquidity shocks reverberation be…
Study allows removing data from machine learning models with strong guarantees.
problem Certifying removal of training data from machine learning models.
method Defined and developed a certified-removal mechanism for linear classifiers.
result Demonstrated that certified removal is possible and practical in certain learning settings.
Wavesplit separates speech from mixtures using clustering.
problem Permutation problem in speech separation.
method End-to-end system infers speaker representations and estimates signals.
result Robust separation of long recordings, new benchmarks set.
This paper develops an active sensing method to estimate the relative weight (or trust) agents place on their neighbors' information in a social network. The model used for the regression is based on the steady state equation in the linear DeGroot model under the influence of stubborn agents, i.e., agents whose opinion…
Selective removal of data subsets can efficiently unlearn unwanted distributions.
problem Efficiently removing unwanted data subsets without losing important information.
method Formalized as distributional unlearning, using Kullback-Leibler divergence constraints to select a small subset of data.
result Proposed method achieves corresponding log-loss bounds and is quadratically more sample-efficient than random removal.
New unsupervised neural network for beamforming masks trained on real noisy speech.
problem Training neural beamforming masks without labeled data.
method Unsupervised training using a likelihood criterion from spatial mixture model.
result Performance comparable to supervised and teacher-trained systems.
Unified framework for explaining models by removing features.
problem Unclear relationships and preferences among existing model explanation methods.
method Removal-based explanations characterized by three dimensions.
result Unified framework unifies 26 existing methods.
The paper proves removable singularity for nonlocal minimal graphs.
problem Proving removable singularities for nonlocal minimal graphs.
method Analyzing (s,1)-capacity zero compact sets to ensure graphs are minimal in the entire domain. result Nonlocal minimal graphs are removable in the entire domain if they are minimal in a set of (s,1)-capacity zero. Unified framework for model explanation methods based on feature removal.
problem Unclear relationships and preferences among various model explanation methods.
method Characterizes removal-based explanations along three dimensions.
result Unified 26 existing methods, including widely used approaches.
Removing spurious features can hurt model accuracy and disproportionately affect different groups.
problem Interference from spurious features in robust model performance across different groups.
method Characterization and analysis of spurious feature removal in noiseless overparameterized linear regression.
result Removal of spurious features can decrease accuracy and disproportionately affect different groups, even in balanced datasets.
Removes singularities for Yang-Mills-Higgs fields in higher dimensions.
problem Yang-Mills-Higgs fields with isolated singularities.
method Establishes decay estimates and conformally invariant energy bounds.
result Removable singularity theorem for Yang-Mills-Higgs fields.
Extends bundle structure in a three-manifold with removable singularities.
problem Point-singularity in Sasakian three-manifold.
method Complex analysis methods.
result Extends bundle structure across removable point-singularity.
New algorithm for mean estimation in add-remove model achieves optimal error.
problem Mean estimation in add-remove model of differential privacy.
method Proposed new algorithm achieving min-max optimality.
result Achieves best possible constant in mean squared error for all ε.
Machine learning confound removal biases results, leading to misleading predictions.
problem Common confound removal methods in machine learning lead to misleading predictions.
method Featurewise removal of confound variance by linear regression before applying ML.
result This common deconfounding approach can leak information, amplifying null or moderate effects.
We call a singularity of a presymplectic form ω removable in its graph if its graph extends to a smooth Dirac structure over the singularity. An example for this is the symplectic form of a magnetic monopole. A criterion for the removability of singularities is given in terms of regularizing functions for pure spinor…
D3M debiases models by selectively removing problematic examples.
problem Model failures on underrepresented subgroups.
method Isolates and removes specific training examples that cause failures.
result Efficiently trains debiased classifiers with minimal example removal.
PUMA augments models to remove unique data points without performance loss.
problem Preserving model performance while removing unique training data points.
method Explicitly models data influence, reweights remaining data optimally.
result PUMA effectively removes unique data points without performance degradation.
In this paper we prove a local removable singularity theorem for certain minimal laminations with isolated singularities in a Riemannian three-manifold. This removable singularity theorem is the key result used in our proof that a complete, embedded minimal surface in R3 with quadratic decay of curvature ha…
This paper proposes a novel Gaussian process approach to fault removal in time-series data. Fault removal does not delete the faulty signal data but, instead, massages the fault from the data. We assume that only one fault occurs at any one time and model the signal by two separate non-parametric Gaussian process model…
Algorithm removes spurious concepts from neural network representations without harming task performance.
problem Spurious correlations hinder neural network out-of-distribution generalization.
method Iterative algorithm that identifies two orthogonal subspaces in neural network representation.
result Algorithm outperforms existing methods on computer vision and natural language processing benchmarks.
Squint bound improved by removing lnlnT term.
problem Improving the Squint bound by removing the lnlnT term. method Using the Krichevsky--Trofimov algorithm to change the prior.
result Removed the lnlnT term from the Squint bound. The study extends removability results for quasiregular curves in Euclidean spaces.
problem Removability of singularities in quasiregular curves.
method Extending a fundamental inequality for volume forms to calibrations and proving a Caccioppoli inequality for quasiregular curves.
result Every non-constant quasiregular curve has infinite energy.
While great progress has been made recently in automatic image manipulation, it has been limited to object centric images like faces or structured scene datasets. In this work, we take a step towards general scene-level image editing by developing an automatic interaction-free object removal model. Our model learns to …
We consider sufficient conditions of local removability of coincidences of maps f,g:N->M, where M,N are manifolds with dimensions dimN>dimM. The coincidence index is the only obstruction to the removability for maps with fibers either acyclic or homeomorphic to spheres of certain dimensions. We also address the normali…
Credit and liquidity risks represent main channels of financial contagion for interbank lending markets. On one hand, banks face potential losses whenever their counterparties are under distress and thus unable to fulfill their obligations. On the other hand, solvency constraints may force banks to recover lost funding…
We prove a removal of singularities result for Bach-flat metrics in dimension 4 under the assumption of bounded L^2 norm of curvature, bounded Sobolev constant and a volume growth bound. This result extends the removal of singularities result for special classes of Bach-flat metrics obtained in \cite{TVMOD}. For the pr…
This study examines how removing edges from complete graphs affects Ollivier Ricci curvature.
problem Conditions under which Ollivier Ricci curvature changes sign after edge removal.
method Defined and analyzed graphs obtained by removing matching, vertex incident, and cycle edges from complete graphs.
result Ollivier Ricci curvature remains positive or zero for graphs formed by removing edges from complete graphs.
Study on Yamabe flow on manifolds with singularities, proving removability.
problem Yamabe flow on manifolds with submanifold singularities.
method Analyzing the Yamabe flow on Riemannian manifolds of dimension m≥3 minus a closed submanifold of dimension n. result Removability of singularities preserved along the Yamabe flow in certain cases.
New method robustly removes malicious nodes from networks considering uncertainty.
problem Detect and remove malicious nodes in networks considering uncertainty and indirect costs.
method Distributionally robust framework using duality and Semidefinite Programming relaxation.
result Algorithm is highly effective and significantly more robust than state of the art.
Minimal surfaces and curves can have singularities removed by isotopy.
problem Removing singularities of minimal surfaces and curves.
method Isotopy through conformal minimal surfaces and null holomorphic curves.
result Branch points and complete ends of finite total curvature can be removed.
Paper explores unsupervised learning for ultrasound image artifact removal.
problem Improving visual quality of ultrasound images from various artifacts.
method Inspired by optimal transport cycleGAN, unsupervised deep learning for artifact removal.
result Unsupervised learning method provides comparable results to supervised learning.
Privacy attacks reveal hidden information in network embeddings after node removal.
problem Privacy concerns in network embeddings after node deletion.
method Analyzed network embeddings and developed an attack to recover removed node information.
result Significant information about removed node's neighbors can be retrieved from remaining embeddings.